Biology subjects

Rzhetsky, A.

Publications and source records attributed to Rzhetsky, A..

8 recordsLinked to original sources

bioRxiv2022

OrganoID: a versatile deep learning platform for organoid image analysis

Organoids have immense potential as ex vivo disease models for drug discovery and personalized drug screening. Dynamic changes in individual organoid morphology, number, and size can indicate important drug responses, however these metrics are difficult and labor-intensive to obtain for high-throughput image datasets. Here, we present OrganoID, a robust image analysis platform that automatically recognizes, labels, and tracks single organoids, pixel-by-pixel, in brightfield and phase-contrast microscopy experiments. The platform was trained on images of pancreatic cancer organoids and validated on separate images of pancreatic, lung, colon, and adenoid cystic carcinoma organoids, which showed excellent agreement with manual measurements of organoid count (96%) and size (95%) without any parameter adjustments. Single-organoid tracking accuracy remained above 89% over a four-day time-lapse microscopy study. Automated single-organoid morphology analysis of a chemotherapy dose-response experiment identified decreased organoid circularity as an important morphological feature reflecting drug response. OrganoID enables straightforward, detailed, and accurate image analysis to accelerate the use of organoids in high-throughput, data-intensive biomedical applications.

bioengineering↗

bioRxiv2021

Targeting Glioblastoma Signaling and Metabolism with A Re-Purposed Brain-Penetrant Drug

The highly lethal brain cancer glioblastoma (GBM) poses a daunting challenge because the blood-brain barrier renders potentially druggable amplified or mutated oncoproteins relatively inaccessible. Here, we identify SMPD1, an enzyme that regulates the conversion of sphingomyelin to ceramide and a critical regulator of plasma membrane structure and organization, as an actionable drug target in glioblastoma. We show that the safe and highly brain-penetrant antidepressant fluoxetine, potently inhibits SMPD1 activity, killing GBMs, in vitro and in patient-derived xenografts, through inhibition of EGFR signaling and via activation of lysosomal stress. Combining fluoxetine with the chemotherapeutic agent temozolomide, a standard of care for GBM patients, causes massive increases in GBM cell death, and complete and long-lived tumor regression in mice. Incorporation of real-world evidence from electronic medical records from insurance databases, reveals significantly increased survival in glioblastoma patients treated with fluoxetine, which was not seen in patients treated with other SSRI anti-depressants. These results nominate the repurposing of fluoxetine as a potentially safe and promising therapy for GBM patients and suggest prospective randomized clinical trials.

cancer biology↗

bioRxiv2020

NERO: A Biomedical Named-entity (Recognition) Ontology with a Large, Annotated Corpus Reveals Meaningful Associations Through Text Embedding

Machine reading is essential for unlocking valuable knowledge contained in the millions of existing biomedical documents. Over the last two decades 1,2, the most dramatic advances in machine-reading have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in machine reading methodology and automated knowledge extraction systems in the same way that ImageNet 4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named-entity analysis tool for biomedicine: (a) a new, Named-Entity Recognition Ontology (NERO) developed specifically for describing entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named-entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named-entity recognition automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.

systems biology↗

bioRxiv2020

Mythical and observable trends in human sex ratio at birth

The human sex ratio at birth (SRB) is defined as the ratio between the number of newborn boys to the total number of newborns per time unit. It is, typically, slightly greater than [Formula] (more boys than girls) and fluctuates over time. In this study, we sought to "myth-check" previously reported associations (and test new hypotheses) using variants of mixed-effect regression analyses and time-series models on two very large electronic health record datasets, representing the populations in the United States and Sweden, respectively. Our results revealed that neither dataset supported models in which the SRB changed seasonally or in response to variations in ambient temperature, and that an increased level of a diverse array of pollutants were associated with lower SRBs. Moreover, we found that increased levels of industrial and agricultural activity, which served as proxies for water pollution, were also associated with lower SRBs.

physiology↗

bioRxiv2018

Pancreatic adenocarcinoma human organoids share structural and genetic features with primary tumors

Patient-derived pancreatic ductal adenocarcinoma (PDAC) organoid systems show great promise for understanding the biological underpinnings of disease and advancing therapeutic precision medicine. Despite the increased use of organoids, the fidelity of molecular features, genetic heterogeneity, and drug response to the tumor of origin remain important unanswered questions limiting their utility. To address this gap in knowledge, we created primary tumor- and PDX-derived organoids, and 2D cultures for in-depth genomic and histopathological comparisons to the primary tumor. Histopathological features and PDAC representative protein markers showed strong concordance. DNA and RNA sequencing of single organoids revealed patient-specific genomic and transcriptomic consistency. Single-cell RNAseq demonstrated that organoids are primarily a clonal population. In drug response assays, organoids displayed patient-specific sensitivities. Additionally, we examined the in vivo PDX response to FOLFIRINOX and Gemcitabine/Abraxane treatments, which was recapitulated in vitro by organoids. The patient-specific molecular and histopathological fidelity of organoids indicate that they can be used to understand the etiology of the patients tumor and the differential response to therapies and suggests utility for predicting drug responses.

cancer biology↗

bioRxiv2017

Conjunction of Factors Triggering Waves of Seasonal Influenza

Understanding the subtle confluence of factors triggering pan-continental, seasonal epidemics of influenza-like illness is an extremely important problem, with the potential to save tens of thousands of lives and billions of dollars every year in the US alone. Beginning with several large, longitudinal datasets on putative factors and clinical data on the disease and health status of over 150 million human subjects observed over a decade, we investigated the source and the mechanistic triggers of epidemics. Our analysis included insurance claims for a significant cross-section of the US population in the past decade, human movement patterns inferred from billions of tweets, whole-US weekly weather data covering the same time span as the medical records, data on vaccination coverage over the same period, and sequence variations of key viral proteins. We also explicitly accounted for the spatio-temporal auto-correlations of infectious waves, and a host of socioeconomic and demographic factors. We carried out multiple orthogonal statistical analyses on these diverse, large geo-temporal datasets to bolster and corroborate our findings. We conclude that the initiation of a pan-continental influenza wave emerges from the simultaneous realization of a complex set of conditions, the strongest predictor groups are as follows, ranked by importance: (1) the host populations socio- and ethno-demographic properties; (2) weather variables pertaining to relevant area specific humidity, temperature, and solar radiation; (3) the virus antigenic drift over time; (4) the host populations land-based travel habits, and; (5) the spatio-temporal dynamics immediate history, as reflected in the influenza wave autocorrelation. The models we infer are demonstrably predictive (area under the Receiver Operating Characteristic curve {approx} 80%) when tested with out-of-sample data, opening the door to the potential formulation of new population-level intervention and mitigation policies.

epidemiology↗

bioRxiv2017

Patchwork of contrasting medication cultures across the USA

Health care in the United States is markedly heterogeneous, with large disparities in treatment choices and health spending. Drug prescription is one major component of health care--reflecting the accuracy of diagnosis, the adherence to evidence-based guidelines, susceptibility to drug marketing, and regulatory factors. Using medical claims data covering nearly half of the USA population, we have developed a framework to compare prescription rates of 600 popular drugs in 2,334 counties. Our approach uncovers geographically separated sub-Americas, where patients receive treatment for different diseases, and where physicians choose different drugs for the same disease. The geographical variation suggests influences of racial composition, state-level health care laws, and wealth. Some regions consistently prefer more expensive drugs, even when they have not been proven more efficacious than cheaper alternatives. Our study underlines the benefit of aggregating massive information on medical practice into a summarized and actionable form. We hope that our methodology and conclusions will guide policy measures for aligning prescriptions with best-practice guidelines.

bioinformatics↗

bioRxiv2017

Universal Microfluidic System for Analysis and Control of Cell Dynamics

Dynamical control of the cellular microenvironment is highly desired for quantitative studies of stem cells and immune signaling. Here, we present an automated microfluidic system for high-throughput culture, differentiation and analysis of a wide range of cells in precisely defined dynamic microenvironments recapitulating cellular niches. This system delivers complex, time-varying biochemical signals to 1,500 independently programmable cultures containing either single cells, 2-D populations, or 3-D organoids, and dynamically stimulates adherent or non-adherent cells while tracking and retrieving them for end-point analysis. Using this system, we investigated the signaling landscape of neural stem cell differentiation under combinatorial and dynamic stimulation with growth factors. Experimental and computational analyses identified \"cellular logic rules\" for stem cell differentiation, and demonstrated the importance of signaling sequence and timing in brain development. This universal platform greatly enhances capabilities of microfluidic cell culture, allows dissection of previously hidden aspects of cellular dynamics, and enables accelerated biological discovery.

bioengineering↗